imbalanced_classification.ipynb

Kernel: Python 3

Imbalanced classification: credit card fraud detection

Author: fchollet
Date created: 2019/05/28
Last modified: 2020/04/17
Description: Demonstration of how to handle highly imbalanced classification problems.

Introduction

This example looks at the Kaggle Credit Card Fraud Detection dataset to demonstrate how to train a classification model on data with highly imbalanced classes.

First, vectorize the CSV data

import csv
import numpy as np

# Get the real data from https://www.kaggle.com/mlg-ulb/creditcardfraud/
fname = "/Users/fchollet/Downloads/creditcard.csv"

all_features = []
all_targets = []
with open(fname) as f:
    for i, line in enumerate(f):
        if i == 0:
            print("HEADER:", line.strip())
            continue  # Skip header
        fields = line.strip().split(",")
        all_features.append([float(v.replace('"', "")) for v in fields[:-1]])
        all_targets.append([int(fields[-1].replace('"', ""))])
        if i == 1:
            print("EXAMPLE FEATURES:", all_features[-1])

features = np.array(all_features, dtype="float32")
targets = np.array(all_targets, dtype="uint8")
print("features.shape:", features.shape)
print("targets.shape:", targets.shape)

Prepare a validation set

num_val_samples = int(len(features) * 0.2)
train_features = features[:-num_val_samples]
train_targets = targets[:-num_val_samples]
val_features = features[-num_val_samples:]
val_targets = targets[-num_val_samples:]

print("Number of training samples:", len(train_features))
print("Number of validation samples:", len(val_features))

Analyze class imbalance in the targets

counts = np.bincount(train_targets[:, 0])
print(
    "Number of positive samples in training data: {} ({:.2f}% of total)".format(
        counts[1], 100 * float(counts[1]) / len(train_targets)
    )
)

weight_for_0 = 1.0 / counts[0]
weight_for_1 = 1.0 / counts[1]

Normalize the data using training set statistics

mean = np.mean(train_features, axis=0)
train_features -= mean
val_features -= mean
std = np.std(train_features, axis=0)
train_features /= std
val_features /= std

Build a binary classification model

import keras

model = keras.Sequential(
    [
        keras.Input(shape=train_features.shape[1:]),
        keras.layers.Dense(256, activation="relu"),
        keras.layers.Dense(256, activation="relu"),
        keras.layers.Dropout(0.3),
        keras.layers.Dense(256, activation="relu"),
        keras.layers.Dropout(0.3),
        keras.layers.Dense(1, activation="sigmoid"),
    ]
)
model.summary()

Train the model with class_weight argument

metrics = [
    keras.metrics.FalseNegatives(name="fn"),
    keras.metrics.FalsePositives(name="fp"),
    keras.metrics.TrueNegatives(name="tn"),
    keras.metrics.TruePositives(name="tp"),
    keras.metrics.Precision(name="precision"),
    keras.metrics.Recall(name="recall"),
]

model.compile(
    optimizer=keras.optimizers.Adam(1e-2), loss="binary_crossentropy", metrics=metrics
)

callbacks = [keras.callbacks.ModelCheckpoint("fraud_model_at_epoch_{epoch}.keras")]
class_weight = {0: weight_for_0, 1: weight_for_1}

model.fit(
    train_features,
    train_targets,
    batch_size=2048,
    epochs=30,
    verbose=2,
    callbacks=callbacks,
    validation_data=(val_features, val_targets),
    class_weight=class_weight,
)

Conclusions

At the end of training, out of 56,961 validation transactions, we are:

• Correctly identifying 66 of them as fraudulent

• Missing 9 fraudulent transactions

• At the cost of incorrectly flagging 441 legitimate transactions

In the real world, one would put an even higher weight on class 1, so as to reflect that False Negatives are more costly than False Positives.

Next time your credit card gets declined in an online purchase -- this is why.